Learning Latent Multi-Criteria Ratings From User Reviews for Recommendations

Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user experiences and highlighting their most valued features. Therefore, researchers have proposed various multi-criteria models to improve recommendation performance. However, most of the existing methods utilize only multi-criteria ratings explicitly provided by the users. Note that explicit multi-criteria ratings are sparse and have the problem of missing values. User reviews, on the other hand, contain richer information of user experiences and reveal multi-dimensional user preferences. Therefore, we propose to use latent multi-criteria ratings generated from user reviews, as opposed to explicit multi-criteria ratings, to provide recommendations and capture latent complex heterogeneous user preferences. Specifically, we propose two novel models for the latent multi-criteria rating generation process: the one-stage model LatentMC-1S that utilizes document hashing method to directly compute latent ratings and the two-stage model LatentMC-2S that uses GRU and Gumbel-Softmax for indirect rating generation. Extensive experiments show that the proposed latent multi-criteria rating approaches outperform explicit ratings across different datasets and performance measures. We also show that latent multi-criteria ratings could be used for imputing missing explicit multi-criteria ratings and thus further improving multi-criteria recommender systems.